Description Usage Arguments Details Value Examples

The function estimates MM constant using input data set and enzyme, substrate concentration, and catalytic constant.

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`method` |
method selection: T=TQ model, F=SQ model(default = T) |

`time` |
observed time interval |

`species` |
observed trajectory of product |

`enz` |
enzyme concentration |

`subs` |
substrate concentration |

`MM` |
initial value of MM constant |

`catal` |
true value of catalytic constant |

`tun` |
tunning constant of MH algorithm (default=2.4) |

`std` |
standard deviation of proposal distribution (if =0, caclulated by Opt. function) |

`nrepeat` |
total number of iteration (default=10000) |

`jump` |
length of distance (default =1) |

`burning` |
lenth of burning period (default =0) |

`MM_m` |
prior mean of gamma prior (default =1) |

`MM_v` |
prior variance of gamma prior (default =10000) |

The function MM_est generates a set of Markov Chain Monte Carlo simulation samples from posterior distribution of MM constant of enzyme kinetics model. Because the function considers MM constant as a parameter to be estimated, the user should input three constants of enzyme concentration, substrate concentration and catalytic constant. prior information for MM constant can be given. The turning constant and standard deviation can be set to controlled proper mixing and acceptance ratio of MM constant from it's posterior distribution. Posterior samples are only stored with fixed interval according to set "jump" to reduce serial correlation The initial iterations are removed for convergence. The burning is set the length of initial iterations.

A vector of posterior samples of Michaelis-Menten constant

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